We would like to develop a technology for cancer genomic characterization which we term "Functional Allelotyping," which is a relatively inexpensive hybridization-based approach to detect quantitative differences in allele-specific expression (ASE) of genes that will be used to distinguish tumors from matched normal tissue. This approach could identify loss of expression (LOE) of one allele of a tumor suppressor gene, as well as activation of the normally silent allele of a tumor promoting gene, such as loss of imprinting (LOI), while also detecting loss of heterozygosity (LOH) of expressed genes. Our first aim is to develop a scalable hybridization-based method for functional allelotyping, based on comparison of allele-specific expression between tumor and matched normal samples. Our second aim is to scale functional allelotyping to the human gene set. Our third aim is to develop statistical tools to distinguish and score LOH, LOE, and LOI. We are encouraged in this effort by our preliminary data that show: the ability to detect quantitative differences in ASE in reconstitution experiments; and the ability already to accurately identify half of allelic imbalances using an array constituting 10% of the human gene set. Functional allelotyping should be valuable to cancer researchers generally, as some of its output cannot currently be obtained any other way at a genome level, such as the discovery of LOI of unknown genes. However, we believe it will be particularly useful for screening samples for further characterization under TCGA, as a way of substantially reducing costs to the project by prioritizing genes, individual tumors, and types of analyses for further investigation. TO PUBLIC HEALTH The proposed work will have a substantial impact on public health, by making it possible to identify high priority genes involved in human cancer, thereby directing time and resources most efficiently in the discovery of new cancer genes. We believe that the work will be an integral part of the tools used by the TCGA program, and will also have independent value to cancer researchers in discovering new tumor suppressor genes and genes abnormally activated in cancer. [unreadable] [unreadable] [unreadable]